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Short-term bitcoin market prediction via machine learning

Jaquart, Patrick 1; Dann, David 1; Weinhardt, Christof ORCID iD icon 1
1 Institut für Wirtschaftsinformatik und Marketing (IISM), Karlsruher Institut für Technologie (KIT)

Abstract:

We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform a random classifier, recurrent neural networks and gradient boosting classifiers are especially well-suited for the examined prediction tasks. We use a comprehensive feature set, including technical, blockchain-based, sentiment-/interest-based, and asset-based features. Our results show that technical features remain most relevant for most methods, followed by selected blockchain-based and sentiment-/interest-based features. Additionally, we find that predictability increases for longer prediction horizons. Although a quantile-based long-short trading strategy generates monthly returns of up to 39% before transaction costs, it leads to negative returns after taking transaction costs into account due to the particularly short holding periods.


Verlagsausgabe §
DOI: 10.5445/IR/1000150665
Veröffentlicht am 04.10.2022
Originalveröffentlichung
DOI: 10.1016/j.jfds.2021.03.001
Scopus
Zitationen: 63
Dimensions
Zitationen: 73
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 11.2021
Sprache Englisch
Identifikator ISSN: 2405-9188
KITopen-ID: 1000150665
Erschienen in The Journal of Finance and Data Science
Verlag Elsevier
Band 7
Seiten 45–66
Nachgewiesen in Dimensions
Scopus
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